{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5e7d7e7d-dbd1-44f5-9d87-a3afcc59e8ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w [3.15636039] b= [0.04433569]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "from sklearn.linear_model import LinearRegression,SGDRegressor\n",
    "x=np.array([[100],[113],[90],[89],[60],[70],[50],[45],[55],[78]])\n",
    "y=np.array([[301],[324],[285],[296],[200],[260],[300],[120],[180],[245]])\n",
    "model=SGDRegressor(loss='huber',max_iter=5000,random_state=42)\n",
    "model.fit(x,y.ravel())\n",
    "print('w',model.coef_,'b=',model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f6c100f3-88ad-43da-bae6-c71a5ebde289",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'matplotlib.pyplot' has no attribute 'scater'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[6], line 7\u001b[0m\n\u001b[0;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfont.sans-serif\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSimhei\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39maxis([\u001b[38;5;241m40\u001b[39m,\u001b[38;5;241m125\u001b[39m,\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m400\u001b[39m])\n\u001b[1;32m----> 7\u001b[0m plt\u001b[38;5;241m.\u001b[39mscater(x,y,s\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m60\u001b[39m,c\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m'\u001b[39m,marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      8\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(x,y2,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr-\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      9\u001b[0m plt\u001b[38;5;241m.\u001b[39mlegend([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m真实值\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m预测值\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'scater'"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "y2=model.predict(x)\n",
    "plt.xlabel('面积')\n",
    "plt.ylabel('售价')\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.axis([40,125,100,400])\n",
    "plt.scater(x,y,s=60,c='k',marker='o')\n",
    "plt.plot(x,y2,'r-')\n",
    "plt.legend(['真实值','预测值'])\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0406aac8-cf12-4d21-bfa6-3125f1805b70",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
